Zementis and Teradata Announce In-database Scoring for Big Data

As a result of its partnership with Teradata, Zementis is excited to announce the availability of the Universal PMML Plug-in (UPPI) for Teradata analytic platforms. It does not get easier than this! Simply deploy your predictive models built in R, IBM SPSS, SAS EM, ... and score your big data, directly in-database, where it resides.

The Zementis Universal PMML Plug-in (UPPI) enables the execution of standards-based predictive analytics directly within the Teradata Unified Data Architecture™. Users can now easily deploy predictive models built in R, IBM SPSS, SAS EM and other popular analytic tools on Aster and/or Teradata to achieve scale. The bridge between these systems is PMML, the Predictive Model Markup Language standard. It allows for models to be instantly moved from the scientist's desktop to the database where they will be executed.

As described by Teradata's Chris Twogood, VP for Product and Services Marketing, "by partnering with Zementis, we are able to offer high performance, enterprise-level predictive analytics scoring for the major analytics tools that support PMML. With Zementis and PMML, we are eliminating the need for customers to recode predictive analytic models in order to deploy them within our database. In turn, this enables an analyst to reduce the time to insight required in most businesses today." Available for Teradata and Teradata Aster databases, UPPI leverages the massively parallel databases as a scalable, high-performance, scoring engine that easily processes through petabyte-scale data volumes. UPPI takes full advantage of the high-performance data warehouse with its massively parallel processing capabilities for rapid execution of standards-based predictive analytics based on the PMML standard. Models built in most commercial and open source data mining tools can now instantly be deployed in Teradata or Aster. The net result is the ability to leverage the power of standards-based predictive analytics on a massive scale, right where the data resides. Read the full press release!

Predictive model deployment with PMML

Model deployment used to be a big task. Predictive models, once built, needed to be re-coded into production to be able to score new data. This process was prone to errors and could easily take up to six months. Re-coding of predictive models has no place in the big data era we live in. Since data is changing rapidly, model deployment needs to be instantaneous and error-free.

PMML, the Predictive Model Markup Language, is the standard to represent predictive models. Given that PMML can be produced by all the top commercial and open-source data mining tools (e.g., FICO Model Builder, SAS EM, IBM SPSS, R, KNIME, ...), a predictive model can be easily moved into the production environment once it is represented as a PMML file.

Zementis offers ADAPA for real-time scoring and UPPI for big data scoring which make the entire model deployment process a no-brainer. Given that ADAPA and UPPI are universal PMML consumers (accept any version of PMML produced by any PMML-compliant tool), they can make predictive models instantly available for execution inside the production environment.

Check out the Zementis website for details.

Making the case for PMML and ADAPA

If you are not familiar with PMML, the Predictive Model Markup Language, you may be wondering what all the fuss is about ...

PMML is the de facto standard to represent data mining and predictive analytic solutions. With PMML, one can easily share a predictive solution among PMML-compliant applications and systems  For example, you can build your model in R, export it in PMML, and use ADAPA, the Zementis Scoring Engine, to deploy it in production.

Many data mining models are a one-time affair. You use historical data to build the model and use it to analyze ... historical data. Wait! That sounds more like descriptive analytics, not predictive analytics. Well, that is sort of true. To be truly predictive, a data mining model needs to be applied to new data. These are the models that need to be operationally deployed and, from my point of view, these are the solutions that are truly revolutionizing the way we do business and live in the Big Data world.

If you want then to use your data mining model to make predictions when presented with new data, it needs to be a dynamic asset. It cannot be static. You need to be able to build it and instantly put it to use. And, that's where PMML and ADAPA come in handy.

Obviously, a few data mining tools try to lock you in. You happily build the model using tool A, just to realize that you need the same tool to execute it. In this case, you are missing out. Here are some of the benefits of moving your predictive model to ADAPA:

• Overcome speed/memory limitations
• Dramatically lower your infrastructure cost
• Tap into all the advantages of cloud computing with ADAPA on the Cloud (IBM SmartCloud or Amazon EC2)
• Produce scores in real-time (using Web Services or Java API), on-demand, or batch-mode
• Execute your models directly from Excel, by using the ADAPA Add-in for Excel
• Benefit from using a set of PMML-compliant model development tools (best of breed)
• Deploy your models in minutes
• Manage models via Web Services or a Web console
• Upload one or many models into ADAPA at once
• Benefit from the seamless integration of business rules and predictive models (yes, for those who need it, ADAPA comes with a business rules engine)

PMML and ADAPA allow you to use best of breed tools (not the same old tool) for the job at hand. Also, you can leverage the expertise from a diverse group of data scientists. That means, not all your data scientists need to be experts on a single tool. They can use different tools that share one thing in common, the PMML standard. And, once represented in PMML, models can be easily understood by all team members. PMML allows for transparency and, in doing so, fosters best practices.

Why not benefit from: 1) an open standard to represent data mining models; and 2) a proven scoring engine that consumes any version of PMML and make it available for execution right away, in real-time?

Keep also in mind that ADAPA's sister product, the Universal PMML Plug-in (UPPI), allows you to move the same PMML file in-database or Hadoop. UPPI is currently available for EMC Greenplum, SAP Sybase IQ, IBM Netezza, and Teradata/Aster. With UPPI for in-database scoring, there is no need to move your data outside the database. Data and models reside inside it and so there is minimal data movement and maximum scoring speed. UPPI is also available for Datameer and will soon be available for Hadoop/Hive.

Making a model operational in minutes has never been easier! And, it is all because of PMML and scoring tools such as ADAPA and UPPI.

The Zementis Partnership with Teradata

The partnership between Zementis and Teradata allows customers with a variety of data mining tools to efficiently deploy predictive models based on the Predictive Model Markup Language (PMML) standard.  Focused on Big Data applications, the Universal PMML Plug-in (UPPI) for Teradata enables scalable execution of standards-based predictive analytics directly within the Teradata data warehouse.

To read more about the benefits of running your predictive solutions inside Teradata and Teradata Aster, please visit:

http://www.teradata.com/templates/Partners/PartnerProfile.aspx?id=12884902321

PMML Scoring

Zementis offers a range of products that make possible the deployment of predictive solutions and data mining models built in all the top commercial and open-source data mining vendors. Our products include the ADAPA Scoring Engine for real-time scoring and UPPI, which is currently available for a host of database platforms as well as Hadoop/Datameer. For a list of available platforms, please visit our in-database products page.

Rationale

Not all analytic tasks are born the same. If one is confronted with massive volumes of data that need to be scored on a regular basis, in-database scoring sounds like the logical thing to do. In all likelihood, the data in this case is already stored in a database and, with in-database scoring, there is no data movement. Data and models reside together hence scores and predictions flow on an accelerated pace

Universal PMML Scoring for Teradata and Aster

Big Data and PMML, the Predictive Model Markup Language, are hot topics these days. But, when combined with in-database scoring, they take a new and powerful meaning. It is then no wonder that Zementis is thrilled to announce its partnership with Teradata, a global leader in data warehousing and analytics.

Teradata and Zementis Zementis is pleased to announce that its Universal PMML Scoring Engine (UPPI) will soon be available on the Teradata and Aster databases. Zementis offers a range of products that make possible the deployment of predictive solutions and data mining models built in all the top commercial and open-source data mining vendors. Our products include the ADAPA Decisioning Engine for real-time scoring and UPPI, which is currently available for a host of database platforms as well as Hadoop/Datameer.

With UPPI for Teradata and UPPI for Aster, Zementis is expanding considerably the number of advanced platforms able to combine in-database scoring and data warehousing for rapid, on-the-fly predictive analytics on large volumes of data.  UPPI for Teradata and UPPI for Aster enable analytic enterprises to realize significant business value from new business models and help companies drive both top-line revenue growth and bottom-line cost savings.    Check out the Zementis website for webinars, presentations and product data sheets and to learn more about in-database scoring with UPPI.